Reconstruction of CT images from sparse-view polyenergetic data using total variation minimization

Recent work in CT image reconstruction has seen increasing interest in the use of compressive sensing techniques to reconstruct images from sparse-view projection data, with the goal of reducing radiation dose as well as scan time. Most often these reconstruction approaches exploit sparsity in the gradient of the image using total variation (TV) minimization. Following the existing theoretical results from compressive sensing, these approaches typically assume a linear measurement model, which corresponds to data generated from a monoenergetic X-ray beam. Most clinical CT systems generate X-rays from a polyenergetic spectrum, however, which is inconsistent with a linear system model and produces the well-known beam hardening artifacts. Such artifacts have been observed in some studies on sparse-view CT reconstruction using a linear model. In this work we incorporate an existing polyenergetic iterative technique known as polyenergetic SART (pSART) into a TV minimization reconstruction algorithm. Using numerical phantom experiments, we demonstrate that this polyenergetic TV minimization algorithm is able to reconstruct images free of both undersampling and beam hardening artifacts from sparse-view, polyenergetic projection data.